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Zhang Y, Qu L, Zhang H, Wang Y, Gao G, Wang X, Zhang T. Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT. Front Med (Lausanne) 2024; 11:1403020. [PMID: 38975053 PMCID: PMC11224554 DOI: 10.3389/fmed.2024.1403020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Background The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures. Methods We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established. Results (1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0. Conclusion We analyzed the risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.
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Affiliation(s)
- Yifan Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Lin Qu
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Haihua Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Ying Wang
- Department of Respiratory Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Guizhou Gao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Xiaodong Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Tao Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
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Ding Y, He C, Zhao X, Xue S, Tang J. Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests. Front Med (Lausanne) 2022; 9:936595. [PMID: 36059824 PMCID: PMC9433577 DOI: 10.3389/fmed.2022.936595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.
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Affiliation(s)
- Yizong Ding
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunming He
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojing Zhao
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Song Xue
- Department of Cardiovascular Surgery, Reiji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Tang
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Jian Tang,
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Papalampidou A, Papoutsi E, Katsaounou P. Pulmonary nodule malignancy probability: a diagnostic accuracy meta-analysis of the Mayo model. Clin Radiol 2022; 77:443-450. [DOI: 10.1016/j.crad.2022.01.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Senent-Valero M, Librero J, Pastor-Valero M. Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review. Syst Rev 2021; 10:308. [PMID: 34872592 PMCID: PMC8650360 DOI: 10.1186/s13643-021-01856-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 11/18/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. METHODS We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. RESULTS A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice. CONCLUSIONS The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020161559.
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Affiliation(s)
- Marina Senent-Valero
- Department of Public Health, History of Science and Gynaecology, Faculty of Medicine, Miguel Hernández University, Sant Joan d'Alacant, Alicante, Spain.
| | - Julián Librero
- Navarrabiomed, Complejo Hospitalario de Navarra, UPNA, Pamplona, Spain
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain
| | - María Pastor-Valero
- Department of Public Health, History of Science and Gynaecology, Faculty of Medicine, Miguel Hernández University, Sant Joan d'Alacant, Alicante, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
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Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules? Radiol Oncol 2021; 55:259-267. [PMID: 34051709 PMCID: PMC8366734 DOI: 10.2478/raon-2021-0024] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 04/24/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT). PATIENTS AND METHODS Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic 18F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUVmax) was measured on conventional 18F-FDG PET/CT images. The influx constant (Ki ) was calculated from the dynamic 18F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test. RESULTS Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUVmax values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average Ki values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613). CONCLUSIONS Dynamic 18F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.
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Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-Based lung cancer screening: A systematic review. Lung Cancer 2020; 147:154-186. [DOI: 10.1016/j.lungcan.2020.07.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022]
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Folch EE, Labarca G, Ospina-Delgado D, Kheir F, Majid A, Khandhar SJ, Mehta HJ, Jantz MA, Fernandez-Bussy S. Sensitivity and Safety of Electromagnetic Navigation Bronchoscopy for Lung Cancer Diagnosis: Systematic Review and Meta-analysis. Chest 2020; 158:1753-1769. [PMID: 32450240 DOI: 10.1016/j.chest.2020.05.534] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 05/01/2020] [Accepted: 05/08/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Bronchoscopy is a useful tool for the diagnosis of lesions near central airways; however, the diagnostic accuracy of these procedures for peripheral pulmonary lesions (PPLs) is a matter of ongoing debate. In this setting, electromagnetic navigation bronchoscopy (ENB) is a technique used to navigate and obtain samples from these lesions. This systematic review and meta-analysis aims to explore the sensitivity of ENB in patients with PPLs suspected of lung cancer. RESEARCH QUESTION In patients with peripheral pulmonary lesion suspected of lung cancer, what is the sensitivity and safety of electromagnetic navigation bronchoscopy compared to surgery or longitudinal follow up? STUDY DESIGN AND METHODS A comprehensive search of several databases was performed. Extracted data included sensitivity of ENB for malignancy, adequacy of the tissue sample, and complications. The study quality was assessed using the QUADAS-2 tool, and the combined data were meta-analyzed using a bivariate method model. A summary receiver operatic characteristic curve (sROC) was created. Finally, the quality of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS Forty studies with a total of 3,342 participants were included in our analysis. ENB reported a pooled sensitivity of 77% (95% CI, 72%-82%; I2 = 80.6%) and a specificity of 100% (95% CI, 99%-100%; I2 = 0%) for malignancy. The sROC showed an area under the curve of 0.955 (P = .03). ENB achieved a sufficient sample for ancillary tests in 90.9% (95% CI, 84.8%-96.9%; I2 = 80.7%). Risk of pneumothorax was 2.0% (95% CI, 1.0-3.0; I2 = 45.2%). We found subgroup differences according to the risk of bias and the number of sampling techniques. Meta-regression showed an association between sensitivity and the mean distance of the sensor tip to the center of the nodule, the number of tissue sampling techniques, and the cancer prevalence in the study. INTERPRETATION ENB is very safe with good sensitivity for diagnosing malignancy in patients with PPLs. The applicability of our findings is limited because most studies were done with the superDimension navigation system and heterogeneity was high. TRIAL REGISTRY PROSPERO; No.: CRD42019109449; URL: https://www.crd.york.ac.uk/prospero/.
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Affiliation(s)
- Erik E Folch
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
| | - Gonzalo Labarca
- Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile
| | - Daniel Ospina-Delgado
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Fayez Kheir
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Adnan Majid
- Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | | | - Hiren J Mehta
- Division of Pulmonary and Critical Care, University of Florida, Gainesville, FL
| | - Michael A Jantz
- Division of Pulmonary and Critical Care, University of Florida, Gainesville, FL
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Cruickshank A, Stieler G, Ameer F. Evaluation of the solitary pulmonary nodule. Intern Med J 2019; 49:306-315. [PMID: 30897667 DOI: 10.1111/imj.14219] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 11/14/2018] [Accepted: 11/22/2018] [Indexed: 12/17/2022]
Abstract
The solitary pulmonary nodule represents a common diagnostic challenge for clinicians. While most are benign, a significant number represent early, potentially curable lung cancers. With the increased utilisation of chest computed tomography, solitary pulmonary nodules are increasingly being identified and with lung cancer screening programmes now on the horizon globally, it is crucial clinicians are familiar with the evaluation and management of solitary pulmonary nodules. Through the evaluation of patient risk factors combined with computed tomography characteristics of solitary pulmonary nodules, including size, growth rate, margin characteristics, calcification, density and location; a clinician can assess the risk of malignancy. This article provides an up to date review of the imaging features of both benign and malignant solitary pulmonary nodules to assist in the identification of nodules that require histological confirmation or ongoing surveillance. In addition, we summarise the newly updated Fleischner Society Guidelines that provide clinicians with a framework for the evaluation and management of solitary pulmonary nodules.
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Affiliation(s)
- Ashleigh Cruickshank
- Department of Respiratory Medicine, Ipswich General Hospital, Brisbane, Queensland, Australia.,Discipline of Medicine, School of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Geoff Stieler
- Department of Radiology, Ipswich General Hospital, Brisbane, Queensland, Australia
| | - Faisal Ameer
- Department of Respiratory Medicine, Ipswich General Hospital, Brisbane, Queensland, Australia.,Discipline of Medicine, School of Medicine, University of Queensland, Brisbane, Queensland, Australia
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Chu ZG, Zhang Y, Li WJ, Li Q, Zheng YN, Lv FJ. Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations. BMC Cancer 2019; 19:1060. [PMID: 31699047 PMCID: PMC6836448 DOI: 10.1186/s12885-019-6274-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/18/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The computed tomography (CT) features of small solid lung cancers and their changing regularity as they grow have not been well studied. The purpose of this study was to analyze the CT features of solid lung cancerous nodules (SLCNs) with different sizes and their variations. METHODS Between February 2013 and April 2018, a consecutive cohort of 224 patients (225 nodules) with confirmed primary SLCNs was enrolled. The nodules were divided into four groups based on tumor diameter (A: diameter ≤ 1.0 cm, 35 lesions; B: 1.0 cm < diameter ≤ 1.5 cm, 60 lesions; C: 1.5 cm < diameter ≤ 2.0 cm, 63 lesions; and D: 2.0 cm < diameter ≤ 3.0 cm, 67 lesions). CT features of nodules within each group were summarized and compared. RESULTS Most nodules in different groups were located in upper lobes (groups A - D:50.8%-73.1%) and had a gap from the pleura (groups A - D:89.6%-100%). The main CT features of smaller (diameter ≤ 1 cm) and larger (diameter > 1 cm) nodules were significantly different. As nodule diameter increased, more lesions showed a regular shape, homogeneous density, clear but coarse tumor-lung interface, lobulation, spiculation, spinous protuberance, vascular convergence, pleural retraction, bronchial truncation, and beam-shaped opacity (p < 0.05 for all). The presence of halo sign in all groups was similar (17.5%-22.5%; p > 0.05). CONCLUSIONS The CT features vary among SLCNs with different sizes. Understanding their changing regularity is helpful for identifying smaller suspicious malignant nodules and early determining their nature in follow-up.
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Affiliation(s)
- Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, China
| | - Yan Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, China.,Department of Radiology, Chongqing Three Gorges Medical College, Chongqing, China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, China
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, China
| | - Yi-Neng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, China.
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Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram. Pulm Med 2019; 2019:4071762. [PMID: 31687208 PMCID: PMC6800929 DOI: 10.1155/2019/4071762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/17/2019] [Accepted: 08/21/2019] [Indexed: 12/26/2022] Open
Abstract
Background The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures. Objectives The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images. Methods A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules. SINRSB CT scans were performed using the same noncontrast enhanced CT protocol and equipment; the malignant nodule data were acquired from several databases. The kurtosis (KUR) and skewness (SKW) values of these tests were determined for the whole volume of each nodule, and the histograms were classified into two basic patterns: peaks or plateaus. Results The mean (MEN) KUR values of the SINRSBs and malignant nodules were 3.37 ± 3.88 and 5.88 ± 5.11, respectively. The receiver operating characteristic (ROC) curve showed that the sensitivity and specificity for distinguishing SINRSBs from malignant nodules were 65% and 66% for KUR values >6, respectively, with an area under the curve (AUC) of 0.709 (p < 0.0001). The MEN SKW values of the SINRSBs and malignant nodules were 1.73 ± 0.94 and 2.07 ± 1.01, respectively. The ROC curve showed that the sensitivity and specificity for distinguishing malignant nodules from SINRSBs were 65% and 66% for SKW values >3.1, respectively, with an AUC of 0.709 (p < 0.0001). An analysis of the peak and plateau histograms revealed sensitivity, specificity, and accuracy values of 84%, 74%, and 79%, respectively. Conclusions KUR, SKW, and histogram shape can help to noninvasively diagnose SINRSBs but should not be used alone or without considering clinical data.
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Yang P. Editorial commentary: meeting a paramount challenge. Transl Lung Cancer Res 2018; 7:S158-S159. [PMID: 29780709 DOI: 10.21037/tlcr.2018.03.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ping Yang
- Department of Health Sciences Research, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
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The Value of 18F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules. BIOMED RESEARCH INTERNATIONAL 2018; 2018:9453967. [PMID: 29789808 PMCID: PMC5896270 DOI: 10.1155/2018/9453967] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 02/22/2018] [Indexed: 12/11/2022]
Abstract
Purpose To establish an 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs). Materials and Methods We retrospectively reviewed 177 consecutive patients who underwent 18F-FDG PET/CT for evaluation of SPNs. The mathematical model was established by logistic regression analysis. The diagnostic capabilities of the model were calculated, and the areas under the receiver operating characteristic curve (AUC) were compared with Mayo and VA model. Results The mathematical model was y = exp(x)/[1 + exp(x)], x = −7.363 + 0.079 × age + 1.900 × lobulation + 1.024 × vascular convergence + 1.530 × pleural retraction + 0.359 × the maximum of standardized uptake value (SUVmax). When the cut-off value was set at 0.56, the sensitivity, specificity, and accuracy of our model were 86.55%, 74.14%, and 81.4%, respectively. The area under the receiver operating characteristic curve (AUC) of our model was 0.903 (95% confidence interval (CI): 0.860 to 0.946). The AUC of our model was greater than that of the Mayo model, the VA model, and PET (P < 0.05) and has no difference with that of PET/CT (P > 0.05). Conclusion The mathematical predictive model has high accuracy in estimating the malignant probability of patients with SPNs.
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Development and validation of a novel diagnostic nomogram model based on tumor markers for assessing cancer risk of pulmonary lesions: A multicenter study in Chinese population. Cancer Lett 2018; 420:236-241. [PMID: 29412152 DOI: 10.1016/j.canlet.2018.01.079] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 01/08/2018] [Accepted: 01/30/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE This study aimed to build a valid diagnostic nomogram for assessing the cancer risk of the pulmonary lesions identified by chest CT. PATIENTS AND METHODS A total of 691 patients with pulmonary lesions were recruited from three centers in China. The cut-off value for each tumor marker was confirmed by minimum P value method with 1000 bootstrap replications. The nomogram was based on the predictive factors identified by univariate and multivariate analysis. The predictive performance of the nomogram was measured by concordance index and calibrated with 1000 bootstrap samples to decrease the overfit bias. We also evaluated the net benefit of the nomogram via decision curve analysis. Finally, the nomogram was validated externally using a separate cohort of 305 patients enrolled from two additional institutions. RESULTS The cut-off for CEA, SCC, CYFRA21-1, pro-GRP, and HE4 was 4.8 ng/mL, 1.66 ng/mL, 1.83 ng/mL, 56.55 pg/mL, and 63.24Lpmol/L, respectively. Multivariate logistic regression model (LRM) identified tumor size, CEA, SCC, CYFRA21-1, pro-GRP, and HE4 as independent risk factors for lung cancer. The nomogram based on LRM coefficients showed concordance index of 0.901 (95% CI: 0.842-0.960; P < 0.001) for lung cancer in the training set and 0.713 (95% CI: 0.599-0.827; P < 0.001) in the validation set. Decision curve analysis reported a net benefit of 87.6% at 80% threshold probability superior to the baseline model. CONCLUSION Our diagnostic nomogram provides a useful tool for assessing the cancer risk of pulmonary lesions identified in CT screening test.
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14
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Yang J, Wang H, Geng C, Dai Y, Ji J. Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules. Biomed Eng Online 2018; 17:20. [PMID: 29415726 PMCID: PMC5803858 DOI: 10.1186/s12938-018-0435-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 01/10/2018] [Indexed: 02/06/2023] Open
Abstract
Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.
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Affiliation(s)
- Jing Yang
- School of Biomedical Engineering, University of Science and Technology of China, Hefei, 230026 People’s Republic of China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 People’s Republic of China
| | - Hailin Wang
- Radiology Department and Interventional Radiology Center, The Fifth Affiliated Hospital of Wenzhou Medical University, Affiliated Lishui Hospital of Zhejiang University, The Central Hospital of Zhejiang Lishui, Lishui, 323000 People’s Republic of China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 People’s Republic of China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 People’s Republic of China
| | - Jiansong Ji
- Radiology Department and Interventional Radiology Center, The Fifth Affiliated Hospital of Wenzhou Medical University, Affiliated Lishui Hospital of Zhejiang University, The Central Hospital of Zhejiang Lishui, Lishui, 323000 People’s Republic of China
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15
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Sakoda LC, Henderson LM, Caverly TJ, Wernli KJ, Katki HA. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions. CURR EPIDEMIOL REP 2017. [PMID: 29531893 DOI: 10.1007/s40471-017-0126-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Purpose of review Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Recent findings Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Summary Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.
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Affiliation(s)
- Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Tanner J Caverly
- Center for Clinical Management Research, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA USA
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD USA
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Ma J, Yang YL, Wang Y, Zhang XW, Gu XS, Wang ZC. Relationship between computed tomography morphology and prognosis of patients with stage I non-small cell lung cancer. Onco Targets Ther 2017; 10:2249-2256. [PMID: 28461759 PMCID: PMC5408946 DOI: 10.2147/ott.s114960] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
This study aimed to explore the relationship between computed tomography morphology and prognosis of patients with stage I non-small cell lung cancer (NSCLC). From May 2009 to May 2011, a total of 124 patients diagnosed with stage I NSCLC were included. All patients had complete chest computed tomography scans. Five-year follow-up was conducted. Univariate and multivariate Cox regression analyses were performed to estimate the prognostic factors for patients with stage I NSCLC. The 5-year survival rate was 67.74% (84/124). The 5-year survival rates of patients with stage T1a, T1b, and T2a were 89.19%, 75.00%, and 41.86%, respectively. The 5-year survival rates of patients with homogeneity, inhomogeneity, vacuole, and cavity were 68.42%, 72.09%, 59.46%, and 83.33%, respectively. The 5-year survival rates of patients with different margin features were 83.33% (slick margin), 79.73% (lobulation sign), and 39.47% (short burr). The 5-year survival rates of patients with normal, halo, vessel convergence, bronchial transection, and vascular bundle thickening were 84.38%, 72.73%, 71.79%, 52.00%, and 47.06%, respectively. The 5-year survival rates of patients with normal and pleura thickening/indentation were 81.93% and 39.02%. Univariate analysis demonstrated that tumor node metastasis staging, tumor margin, tumor periphery, and pleural invasion were related to the prognosis of stage I NSCLC patients. Cox regression analysis confirmed that T2a stage, pleura thickening/indentation were independent risk factors for poor prognosis of stage I NSCLC. In conclusion, our findings indicate that T2a stage, pleura thickening/indentation might be prognostic factors in stage I NSCLC.
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Affiliation(s)
- Jun Ma
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing.,Department of Radiology
| | - Yun-Long Yang
- Department of Thoracic Surgery, The Affiliated Hospital of Beihua University, Jilin, People's Republic of China
| | | | | | | | - Zhen-Chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing
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17
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Brioude G, Brégeon F, Trousse D, Flaudrops C, Secq V, De Dominicis F, Chabrières E, D'journo XB, Raoult D, Thomas PA. Rapid Diagnosis of Lung Tumors, a Feasability Study Using Maldi-Tof Mass Spectrometry. PLoS One 2016; 11:e0155449. [PMID: 27228175 PMCID: PMC4881980 DOI: 10.1371/journal.pone.0155449] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 04/28/2016] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Despite recent advances in imaging and core or endoscopic biopsies, a percentage of patients have a major lung resection without diagnosis. We aimed to assess the feasibility of a rapid tissue preparation/analysis to discriminate cancerous from non-cancerous lung tissue. METHODS Fresh sample preparations were analyzed with the Microflex LTTM MALDI-TOF analyzer. Each main reference spectra (MSP) was consecutively included in a database. After definitive pathological diagnosis, each MSP was labeled as either cancerous or non-cancerous (normal, inflammatory, infectious nodules). A strategy was constructed based on the number of concordant responses of a mass spectrometry scoring algorithm. A 3-step evaluation included an internal and blind validation of a preliminary database (n = 182 reference spectra from the 100 first patients), followed by validation on a whole cohort database (n = 300 reference spectra from 159 patients). Diagnostic performance indicators were calculated. RESULTS 127 cancerous and 173 non-cancerous samples (144 peripheral biopsies and 29 inflammatory or infectious lesions) were processed within 30 minutes after biopsy sampling. At the most discriminatory level, the samples were correctly classified with a sensitivity, specificity and global accuracy of 92.1%, 97.1% and 95%, respectively. CONCLUSIONS The feasibility of rapid MALDI-TOF analysis, coupled with a very simple lung preparation procedure, appears promising and should be tested in several surgical settings where rapid on-site evaluation of abnormal tissue is required. In the operating room, it appears promising in case of tumors with an uncertain preoperative diagnosis and should be tested as a complementary approach to frozen-biopsy analysis.
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Affiliation(s)
- Geoffrey Brioude
- Service de chirurgie thoracique et des maladies de l'œsophage, Pôle cardio-vasculaire et thoracique, Centre Hospitalo-Universitaire Nord, Assistance publique-Hôpitaux de Marseille, Aix-Marseille université, Marseille, France
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
| | - Fabienne Brégeon
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
- Service desExplorations Fonctionnelles Respiratoires Centre Hospitalo-Universitaire Nord, Pôle cardio-vasculaire et thoracique, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Delphine Trousse
- Service de chirurgie thoracique et des maladies de l'œsophage, Pôle cardio-vasculaire et thoracique, Centre Hospitalo-Universitaire Nord, Assistance publique-Hôpitaux de Marseille, Aix-Marseille université, Marseille, France
| | - Christophe Flaudrops
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
| | - Véronique Secq
- Service d'anatomie pathologique, hôpital Nord, Aix Marseille université, Marseille, France
| | - Florence De Dominicis
- Service de chirurgie thoracique et des maladies de l'œsophage, Pôle cardio-vasculaire et thoracique, Centre Hospitalo-Universitaire Nord, Assistance publique-Hôpitaux de Marseille, Aix-Marseille université, Marseille, France
| | - Eric Chabrières
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
- Pôle des Maladies Infectieuses et Tropicales Clinique et Biologique, Fédération de Bactériologie-Hygiène-Virologie,Centre Hospitalo-Universitaire Timone, Assistance publique des hôpitaux de Marseille, Marseille, France
| | - Xavier-Benoit D'journo
- Service de chirurgie thoracique et des maladies de l'œsophage, Pôle cardio-vasculaire et thoracique, Centre Hospitalo-Universitaire Nord, Assistance publique-Hôpitaux de Marseille, Aix-Marseille université, Marseille, France
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
| | - Didier Raoult
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
- Pôle des Maladies Infectieuses et Tropicales Clinique et Biologique, Fédération de Bactériologie-Hygiène-Virologie,Centre Hospitalo-Universitaire Timone, Assistance publique des hôpitaux de Marseille, Marseille, France
| | - Pascal-Alexandre Thomas
- Service de chirurgie thoracique et des maladies de l'œsophage, Pôle cardio-vasculaire et thoracique, Centre Hospitalo-Universitaire Nord, Assistance publique-Hôpitaux de Marseille, Aix-Marseille université, Marseille, France
- Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, URMITE CNRS 7278 IRD 198 INSERM U1905, IHU Méditerranée Infection, Faculté de Médecine, Aix-Marseille Université, Marseille, France
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18
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Perandini S, Soardi G, Motton M, Dallaserra C, Montemezzi S. Limited value of logistic regression analysis in solid solitary pulmonary nodules characterization: A single-center experience on 288 consecutive cases. J Surg Oncol 2014; 110:883-7. [DOI: 10.1002/jso.23730] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 06/25/2014] [Indexed: 12/21/2022]
Affiliation(s)
- S. Perandini
- Azienda Ospedaliera Universitaria Integrata di Verona; Piazzale Stefani 1 Verona Italy
| | - G.A. Soardi
- Azienda Ospedaliera Universitaria Integrata di Verona; Piazzale Stefani 1 Verona Italy
| | - M. Motton
- Azienda Ospedaliera Universitaria Integrata di Verona; Piazzale Stefani 1 Verona Italy
| | - C. Dallaserra
- Azienda Ospedaliera Universitaria Integrata di Verona; Piazzale Stefani 1 Verona Italy
| | - S. Montemezzi
- Azienda Ospedaliera Universitaria Integrata di Verona; Piazzale Stefani 1 Verona Italy
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